从文本到诊断:聊天在医疗决策中的有效性
Yaroslav Mykhalko1, Pavlo Kish1, Yelyzaveta Rubtsova1
1UZHHOROD NATIONAL UNIVERSITY, UZHHOROD, UKRAINE.
概括
聊天GPT在临床医学中显示出作为诊断工具的巨大潜力,在结构化场景中达到高精度. 需要进一步的研究,以优化其使用,补充数据和快速工程.
科学领域:
- 人工智能在医学中的应用
- 临床诊断 临床诊断 临床诊断
- 自然语言处理自然语言处理.
背景情况:
- 人工智能 (AI) 在医疗保健中的整合正在迅速发展.
- 大型语言模型 (LLM) 显示出有助于医学诊断的前景.
- 评估像ChatGPT这样的LLM的诊断能力对于其临床应用至关重要.
研究的目的:
- 在各种临床场景中评估ChatGPT (特别是ChatGPT-3.5) 的诊断准确性.
- 将ChatGPT的诊断性能与人类医生进行比较.
- 探索不同提示策略对AI诊断能力的影响.
主要方法:
- 利用50个不同的临床病例进行评估.
- 采用了使用ChatGPT-3.5.5的三阶段实验设计.
- 第一个阶段:详细的病例描述与"人格模式"提示符.
- 第二阶段:有错误的案例,提供选择的潜在诊断.
- 第三阶段:在有限的初始信息下模仿医生的决策.
主要成果:
- 在第一阶段,ChatGPT实现了66.00%的准确性,几乎超过了50%的医生.
- 在第二阶段,ChatGPT的准确率达到70.59%,而医生的准确率为41.47%.
- 第1阶段和第2阶段的准确性达到90.00%;第3阶段的准确性达到46.00%.
结论:
- 作为临床医学中的诊断辅助工具,ChatGPT具有很大的潜力,特别是在结构化的环境中.
- 该研究强调了补充数据和复杂的快速工程对于AI诊断的重要性.
- 这些发现有助于理解AI在临床决策中的作用,并确定未来发展的领域.
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